Responsibilities

The Tik Tok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in Tik Tok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.

We are looking for talented individuals to join us for an internship in 2026. Internships at Tik Tok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at Tik Tok.

Internships at Tik Tok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to Tik Tok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible. Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:

• May 11th, 2026
• May 18th, 2026
• May 26th, 2026
• June 8th, 2026
• June 22nd, 2026

Key Responsibilities:

1. Design and implement real-time and offline data architecture for large-scale recommendation systems.
2. Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
3. Collaborate with ML platform teams to support Py Torch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.
4. Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.

Qualifications

Minimum Qualifications:

• Currently pursuing an Undergraduate/Master in Computer Science or a related technical discipline
• Able to commit to working for 12 weeks during Summer 2026
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
• Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.

Preferred Qualifications:

• Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.
• Familiarity with feature storage and training data pipelines, and their integration with Py Torch, especially for large-scale model training.
• Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.
• Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.
• Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.
• Solid understanding of Apache Flink internals, with hands-on experience in state management, connectors, or UDFs.

Additional Information

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy